McDonald’s has been the subject of a global boycott movement due to the company’s assumed approach and behavior amid the geopolitical tension in the Middle East. During this time, implementing sentiment analysis becomes crucial for McDonald's to understand public perception and monitor brand reputation across social media platforms. Although sentiment analysis has been utilized to examine boycott campaigns broadly, current research predominantly utilizes singular model architectures. It fails to provide a targeted analysis of the effects on individual brands. Moreover, previous research has primarily concentrated on English-language content, resulting in a deficiency in comprehending sentiment patterns within varied linguistic environments. Therefore, this study presents a comprehensive evaluation of deep learning and traditional machine learning approaches for classification, comparing the performance of a Convolutional Neural Network (CNN) with Support Vector Machines (SVM) implementing different kernel fun